Seurat (version 2.3.3)

HTODemux: Demultiplex samples based on data from cell 'hashing'

Description

Assign sample-of-origin for each cell, annotate doublets.

Usage

HTODemux(object, assay.type = "HTO", positive_quantile = 0.99,
  init_centers = NULL, cluster_nstarts = 100, k_function = "clara",
  nsamples = 100, print.output = TRUE)

Arguments

object

Seurat object. Assumes that the hash tag oligo (HTO) data has been added and normalized.

assay.type

Name of the Hashtag assay (HTO by default)

positive_quantile

The quantile of inferred 'negative' distribution for each hashtag - over which the cell is considered 'positive'. Default is 0.99

init_centers

Initial number of clusters for hashtags. Default is the # of hashtag oligo names + 1 (to account for negatives)

cluster_nstarts

nstarts value for k-means clustering (for k_function = "kmeans"). 100 by default

k_function

Clustering function for initial hashtag grouping. Default is "clara" for fast k-medoids clustering on large applications, also support "kmeans" for kmeans clustering

nsamples

Number of samples to be drawn from the dataset used for clustering, for k_function = "clara"

print.output

Prints the output

Value

The Seurat object with the following demultiplexed information stored in the meta data:

hash_maxID

Name of hashtag with the highest signal

hash_secondID

Name of hashtag with the second highest signal

hash_margin

The difference between signals for hash_maxID and hash_secondID

hto_classification

Classification result, with doublets/multiplets named by the top two highest hashtags

hto_classification_global

Global classification result (singlet, doublet or negative)

hash_ID

Classification result where doublet IDs are collapsed

Examples

# NOT RUN {
object <- HTODemux(object)
# }